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NASA NTRS2021

Micromechanics Modeling of Textiles for Re-Entry Parachute Applications

Recent flight test projects and NASA missions have highlighted the challenges associated with accurately and efficiently modeling the behavior of parachute deployment systems needed for parachute design. Moreover, parachute deployment has been identified as one of the higher risk components for such missions. The analysis of textile fabrics used for atmospheric entry is inherently complex due to the multiple scales present in the fabric structure, including individual fiber filaments at the microscale, yarn bundles of fibers at the mesoscale, and the overall woven fabric at the macroscale. Computational tools for simulating fabric behavior must be able to account for the different mechanisms present at each scale without sacrificing computational efficiency. This work examines the generalized multiscale method of cells micromechanics theory, which has previously been used for the analysis of reinforced composite structures, to unreinforced textile fabrics. Modifications to the existing composite multiscale framework, implemented in NASA’s Multiscale Analysis Tool (NASMAT), include the specific mechanics unique to unreinforced textile fabrics, and overcoming the assumptions of a fixed fiber angle. It looks to assess the feasibility of using the NASMAT tool for efficient prediction of the response of unreinforced fabrics to loading such that it can ultimately be applied to fluid structure interaction tools for the prediction of parachute deployment systems. In this work, fabric behavior is simulated in NASMAT through homogenization of a triply periodic repeating unit cell, where the geometry of the subcells can change as a function of loading to represent the relative rotation and uncrimping that can occur in fabric tows. Predictions from the amended NASMAT code are compared to experimental data for uniaxial and off-axis tension to verify the ability of the code to incorporate lower-scale mechanics in prediction of unreinforced fabrics under loading.

Micromechanics↗

NASA NTRS2017

Interaction of Delaminations and Matrix Cracks in a CFRP Plate, Part II: Simulation Using an Enriched Shell Finite Element Model

Numerical simulations are presented of a recently developed test which creates multiple delaminations in a CFRP laminate specimen that grow and interact via transverse matrix cracks. A novel shell element enriched with the Floating Node Method, and a damage algorithm based on the Virtual Crack Closure Technique, were used to successfully simulate the tests. Additionally, a 3D high mesh fidelity model based on cohesive zones and continuum damage mechanics was used to simulate the tests and act as a representative of other similar state-of-the-art high mesh fidelity modeling techniques to compare to the enriched shell element. The enriched shell and high mesh fidelity models had similar levels of accuracy and generally matched the experimental data. With runtimes of 36 minutes for the shell model and 55 hours for the high mesh fidelity model, the shell model is 92 times faster than the high- fidelity simulation.

Mark W McElroy↗

NASA NTRSDate not supplied

Predicting Fiber Failure of Plain Weave Fabric with Recursive Multiscale Micromechanics

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Failure↗

NASA NTRSDate not supplied

Multiscale Prediction of Yarn Pullout Failure Mode in Unreinforced Textile Fabrics

Unreinforced woven fabrics have been implemented in a variety of high performance applications, including body armor, deployable structures, and as the reinforcement material in composites. Multiscale modeling techniques have significantly improved the capabilities of simulation-based tools to capture fabric mechanics efficiently and accurately, but often lack in their prediction of failure and require pairing with finite element analysis (FEA) software, limiting their application to the design of ‘fit-for-purpose’ materials. NASA’s Multiscale Analysis Tool (NASMAT) is a standalone multiscale program that has been traditionally used in the analysis of reinforced composites materials. More recently, it has been amended to simulate unreinforced fabric behavior by allowing the geometric state of the tows to change with applied loading due to the lack of a reinforcement material, such as the matrix seen in composites. Previous work has shown the ability of NASMAT to capture nonlinear macroscale behavior by predicting geometric changes in the state of each subcell as a function of the applied loading and allowing each subcell in the analysis to rotate according to these predicted changes, as well as predict nonlinear behavior due to the fiber breakage failure mode. In this work, the capability of predicting the onset and propagation of failure in plain woven fabrics in NASMAT is presented for the yarn pullout failure mode, which occurs when a fabric is loaded at an off-axis angle relative to the warp of weft tow direction. Yarn pullout behavior is initiated by determining the applied load in which the shear resistance of the contact area between yarn families is overcome. When failure is initiated, yarn pullout is determined to have occurred when the applied displacement, calculated from global strain, exceeds the deformed position of a given contact points between yarn families, determined from pin-joint kinematics. Contact points where pullout has occurred contribute to a global damage parameter used to modify the homogenized stiffness of the fabric, resulting in nonlinear behavior observed at the macroscale. The off-axis loading behavior and yarn pullout failure theory have been developed and implemented into NASMAT such that users can simulate off-axis tensile behavior of fabrics in a single, standalone multiscale tool. Simulations are compared to uniaxial tensile tests at various off-axis angles to demonstrate the capability of the tool in its prediction of both the onset of failure at each off-axis angles and the stress-strain behavior as failure progresses.

Materials↗

NASA NTRSDate not supplied

Accelerating the Deployment of Space-Based Quantum Sensors

Quantum sensors rely on quantum mechanical properties, such as wave-particle duality, atomic energy levels, or entanglement, to measure physical, optical, or electromagnetic information from a target. Examples include Rydberg sensors, nitrogen-vacancy magnetometers, HOM (Hong-Ou-Mandel) interferometers, and SQUIDs (Superconducting Quantum Interference Devices). In general, as quantum sensors rely on interrogation at the atomic and photonic level rather than on classical mechanical measurements, these technologies promise advantages over their traditional counterparts in sensitivity, accuracy, and SWaP (Size, Weight, and Power). Conceptually, the use of quantum sensors unlocks the new phenomenology of sensor entanglement. While an ultra-low TRL (Technology Readiness Level) prospect for most sensor types, multi-sensor entanglement is a high risk, high reward strategy with a potential for exquisite sensitivity and SWaP improvements entirely inaccessible to classical sensors.

Grace Caroline Wusk↗

NASA NTRSDate not supplied

Cross-Government Development of Space-Based Quantum Sensors for Earth and Space Science

Quantum sensing technologies promise advantages over their traditional counterparts in terms of sensitivity and accuracy, including higher signal-to-noise ratios, better measurement resolution and sensitivity, and accuracies that can be tied to fundamental constants. As a result of the potentially promising advantages of quantum sensing technologies, the multi-agency Space Science and Technology Partnership Forum (S&T Forum), consisting of NASA, NRO, and USSF, conducted a 2023 study to identify the current state of space-based quantum sensing technology development that will enable cutting-edge Earth and space science. The findings contained within this work provide insight into quantum sensing and quantum enabling technology work across the S&T Forum to inform technology development and identify potential government, industry, and academic coordination and collaboration opportunities in quantum sensing within the Earth and space science community. The results herein focus on the collation of quantum sensing technologies, facilities, and developers captured from the participants of the study to provide a broad overview of quantum sensor development for the Earth and space science community; specifically highlighting how five particular areas of quantum sensing technology (listed in alphabetical order) are of near-term interest to all three S&T Forum agencies: atomic clocks, atom interferometers, quantum magnetometers, Rydberg sensors, and single photon detectors. The findings in this work point to the intent of the S&T Forum agencies to lean into the development of quantum sensing technology in which the three agencies have mutual near-term interest, including cross-cutting enabling component technologies. The information presented provides points of entry in terms of people, facilities, and quantum sensing technology work within the government as well as increased transparency into the five quantum sensing technology areas of mutual interest of the S&T Forum agencies to remove barriers and encourage participation and partnership with the Earth and space science community.

Quantum↗

NASA NTRSDate not supplied

Accelerating the Deployment of Space-Based Quantum Sensors

The Space Science and Technology Partnership Forum (S&T Forum), comprised of an interagency team from the National Aeronautics and Space Administration, the National Reconnaissance Office, and the United States Space Force, presents findings and recommendations from a study on the state of quantum sensors for space applications, enabling component technologies, facilities, and developers across government, industry, and academia. The following five areas of quantum sensing technology (listed in alphabetical order) were identified as being of near-term interest to all three S&T Forum agencies: atomic clocks, atom interferometers, quantum magnetometers, Rydberg sensors, and single photon detectors. This paper focuses on two recommendations: 1) increased collaboration between the S&T Forum, industry, and academia on the development of enabling components for the five quantum sensing technology areas, and 2) challenges and future opportunities to take quantum sensors from the laboratory to space. Through this study, the S&T Forum found a diverse and broad community of quantum sensing developers in the United States that are leading efforts in research, testing, manufacturing, and qualification of quantum sensors and their enabling component technologies. There are common needs across quantum sensing technology areas. To address the common needs, the S&T Forum agencies are encouraged to utilize existing models and mechanisms to enable co-development of enabling component technologies and to explore new avenues for engagement with the quantum sensing community.

Grace C Ray↗

NASA NTRSDate not supplied

Accelerating the Deployment of Space-Based Quantum Sensors

The Space Science and Technology Partnership Forum (S&T Forum), comprised of an interagency team from the National Aeronautics and Space Administration, the National Reconnaissance Office, and the United States Space Force, presents findings and recommendations from a study on the state of quantum sensors for space applications, enabling component technologies, facilities, and developers across government, industry, and academia. The following five areas of quantum sensing technology (listed in alphabetical order) were identified as being of near-term interest to all three S&T Forum agencies: atomic clocks, atom interferometers, quantum magnetometers, Rydberg sensors, and single photon detectors. This paper focuses on two recommendations: 1) increased collaboration between the S&T Forum, industry, and academia on the development of enabling components for the five quantum sensing technology areas, and 2) challenges and future opportunities to take quantum sensors from the laboratory to space. Through this study, the S&T Forum found a diverse and broad community of quantum sensing developers in the United States that are leading efforts in research, testing, manufacturing, and qualification of quantum sensors and their enabling component technologies. There are common needs across quantum sensing technology areas. To address the common needs, the S&T Forum agencies are encouraged to utilize existing models and mechanisms to enable co-development of enabling component technologies and to explore new avenues for engagement with the quantum sensing community.

Grace C Ray↗